Efficient enumeration of frequent sequences
Proceedings of the seventh international conference on Information and knowledge management
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Continually evaluating similarity-based pattern queries on a streaming time series
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Online Data Mining for Co-Evolving Time Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Clustering of time-series subsequences is meaningless: implications for previous and future research
Knowledge and Information Systems
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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In this paper, we deal with mining sequential patterns in multiple time sequences. Building on a state-of-the-art sequential pattern mining algorithm PrefixSpan for mining transaction databases, we propose MILE (MIning in muLtiple sEquences), an efficient algorithm to facilitate the mining process. MILE recursively utilizes the knowledge of existing patterns to avoid redundant data scanning, and therefore can effectively speed up the new patterns' discovery process. Another unique feature of MILE is that it can incorporate prior knowledge of the data distribution in time sequences into the mining process to further improve the performance. Extensive empirical results show that MILE is significantly faster than PrefixSpan. As MILE consumes more memory than PrefixSpan, we also present a solution to trade time efficiency in memory constrained environments.